|Year : 2019 | Volume
| Issue : 2 | Page : 61-69
Risk factors affecting the length of intensive care unit stay after brain tumor surgery
Selda Kayaalti1, Omer Kayaalti2
1 Department of Anesthesiology and Reanimation, Develi Public Hospital, Develi, Kayseri, Turkey
2 Department of Computer Technologies, Develi Huseyin Sahin Vocational College, Kayseri University, Kayseri, Turkey
|Date of Submission||02-Nov-2019|
|Date of Acceptance||13-Jun-2019|
|Date of Web Publication||28-Aug-2019|
Dr. Selda Kayaalti
Develi Public Hospital Camiicedit Neighborhood, Hastane Street No: 14, 38400, Develi, Kayseri
Source of Support: None, Conflict of Interest: None
Aims: In recent years, the number of surgical procedures performed in high-risk patients has increased, and the need for postoperative intensive care has also increased. In this study, it is aimed to identify the risk factors that can be used to estimate the need for intensive care stay of more than 1 day for patients with brain tumor resection.
Methods: In this study, an open-accessible dataset was used, which included preoperative, perioperative, and intensive care follow-up data of 400 patients who were admitted to intensive care unit (ICU) after craniotomy due to brain tumor. The patients were divided into two groups according to the length of stay in the ICU. Patients who had less than a day stay were included in the short-term intensive care need (SICN) group and those staying more than 1 day were included in the long-term intensive care need (LICN). The effect of patients' data on ICU length of stay in ICU was investigated by logistic regression analysis.
Results: Thirty-nine (9.75%) patients and 361 (90.25%) patients were assigned to the LICN group and SICN group, respectively. In the multivariate binary logistic regression model, the increase in total intravenous anesthesia (TIVA) and patient-controlled analgesia (PCA) applications decreases the patients' LICN likelihood while being intubated at ICU admission, need of mechanical ventilation (MV), postoperative hematoma formation, and increased duration of anesthesia increase the patients' LICN likelihood.
Conclusions: The results of our study showed that the likelihood of patients' stay in ICU for more than 1 day could be estimated by such parameters as anesthesia duration, TIVA application, use of PCA device, being intubated at ICU admission, MV requirement, and postoperative hematoma formation.
Keywords: Brain tumor resection, craniotomy, critical care, intensive care unit, length of stay, risk factors
|How to cite this article:|
Kayaalti S, Kayaalti O. Risk factors affecting the length of intensive care unit stay after brain tumor surgery. Indian Anaesth Forum 2019;20:61-9
|How to cite this URL:|
Kayaalti S, Kayaalti O. Risk factors affecting the length of intensive care unit stay after brain tumor surgery. Indian Anaesth Forum [serial online] 2019 [cited 2019 Sep 22];20:61-9. Available from: http://www.theiaforum.org/text.asp?2019/20/2/61/265636
| Introduction|| |
In most of the surgical patients admitted in intensive care unit (ICU), the need for intensive care cannot be anticipated preoperatively. Although the ICU admission, stay, and discharge criteria have been defined by the Intensive Care Society, the use of these criteria in surgical patients is not very common. Therefore, various prognostic scores that can be used especially for surgical patients were formed., Estimating the duration of ICU stay of these patients can help in optimal use of intensive care beds and prevent unnecessary cancellation of operations due to the need for postoperative intensive care.,
The need and the length of ICU stay of patients undergoing surgery for brain tumors are controversial. Following surgery, close monitoring of the patient provides hemodynamic stability and early detection of complications. Most patients, admitted to the ICU after brain tumor surgery, are transferred to the service after 1 day of intensive care admission. The aim of this study is to determine the parameters that will predict the need for ICU stay for more than 1 day.
| Methods|| |
This is a retrospective study of patients, undergoing intensive care after elective craniotomy and performed in accordance with the Helsinki Declaration principles. In our study, a public data set of a prospective study conducted by Huang et al. was used. This data set belongs to the study performed in Beijing Tiantan Hospital (Beijing, China) in 2015 and includes follow-up data of 400 adult patients undergoing elective craniotomy under general anesthesia. The study of Huang et al. was approved by the Institutional Review Board of Beijing Tiantan Hospital (KY2014-034-01) and recorded in ClinicalTrials.gov with the protocol number NCT02318199. Written informed consent was obtained from the patients included in the study or from the health decision-makers that they had previously determined. During the study period, a total of 1281 patients underwent elective craniotomy, 497 of whom were admitted to ICU and 97 were excluded due to exclusion criteria. Exclusion criteria for the study were as follows: (1) patients younger than 18 years or older than 80 years, (2) presence of preoperative consciousness disorder, and (3) 24-h interval between the end of the operation and ICU admission.
All operations were performed by general anesthesia including inhalation anesthesia or total intravenous anesthesia (TIVA). The choice of anesthetic agent was determined by the anesthetist, and the decision for postoperative ICU admission was taken by the surgeon and anesthetist together. The criteria used in the decision phase were determined based on the following conditions: (1) being older than 65 years, (2) being American Society of Anesthesiologists (ASA) physical status III or above, (3) having tumor that was large or localized in the brain stem, (4) preoperative midline shift, and (5) expected long surgical or extubation time. Apart from these criteria, the surgeon and anesthesiologist also decided together on the need for intensive care, which was not planned before the operation. In the ICU, hourly Glasgow Coma Scale (GCS) and pupillary follow-up were performed by nurses. Postoperative pain control was provided by patient-controlled analgesia (PCA) in most patients. The mixture to be administered for PCA was prepared by adding 100 μg sufentanil and 10 mg tropisetron in 100 ml of 0.9% sodium chloride solution. The basal PCA infusion rate was set at 2 ml/h. Infusion was initiated after the patient regained consciousness and the cardiorespiratory stability was achieved. In the first postoperative 6 h, imaging was performed by computed tomography (CT).
Data were divided as initial-preoperative, perioperative, and intensive care follow-up and were recorded. Patients' initial-preoperative data such as demographic data, ASA classification, concomitant diseases (hypertension, coronary artery disease [CAD], arrhythmia, ischemic stroke, and diabetes), smoking, alcohol use, long-term antidepressant, or benzodiazepine use history were taken from the hospital records. Data regarding the tumor type were taken from the pathology report during follow-up. Perioperative data were taken from anesthesia and surgical records. These data include the localization of the tumor (supratentorial or infratentorial), surgical approach (frontal or nonfrontal), duration and method of anesthesia (balanced or TIVA), fluid balance, amount of bleeding, transfusion requirement, intraoperative mannitol or steroid use, and intraoperative hypotension. GCS, body temperature, presence of endotracheal tube during ICU admission, need for mechanical ventilation (MV), central venous catheterization, external ventricular drainage tube presence, PCA device use, oxygen saturation (SpO2) level below 90%, serum glucose level ≥10 mmol/l, and the pain situation of the patients were obtained from the nurse records in the ICU.
Patients were followed until discharge from hospital or death or until 90 days after ICU admission. Follow-up data such as length of stay in ICU, self-extubation, accidental catheter removal, duration of MV, endotracheal tube duration, extubation time, reintubation, unexpected reoperation in the first 72 h, and retrospective CT analyzes were obtained from hospital records and nurse records. Patients were divided into three groups on the basis of endotracheal tube and MV requirement: (1) patients who were extubated immediately at the end of the operation, (2) intubated patients who did not need MV, and (3) intubated patients who needed MV. Therefore, the duration of endotracheal tube is considered to be a separate parameter except for the duration of MV. In addition, long-term results such as hospital stay duration and modified Rankin Scale (mRS) were recorded at the end of follow-up (those with mRS 5–6 were identified with poor neurological results). Up-to-date form of mRS that is developed by Farrell et al. is used in the postoperative neurological evaluation of patients.
In GCS scoring 8 and less indicates severe brain injury, 9–12 indicates moderate injury, and 13–15 indicates mild injury. In our study, using the same ranges for the patients with head trauma, we classified the patients as high-risk group if the GCS score was <8, moderate risk group if the GCS score was between 9 and 12, and low-risk group if the GCS score was between 13 and 15.
Several studies have been carried out on the factors affecting the length of stay in ICU. In these studies, the patients were generally divided into two groups: prolonged and normal intensive care period. The cutoff value separating the two groups generally varies between 1 day and 7 days. In our study, the cutoff value was 1 day because most of the patients (91.25%) who were admitted to the ICU after brain tumor resection were returned to normal service after 1 day. The patients were divided into two groups according to the length of stay in ICU. Patients in need of intensive care for <1 day were classified as short-term intensive care need (SICN) group and those in need of intensive care more than 1 day were classified as long-term intensive care need (LICN) group.
SPSS 22.0 (Statistical Package of Social Sciences Inc., Chicago, IL, USA) program was used in the statistical analyses. Mean, standard deviation, median, minimum, maximum, frequency, and ratio values were used in the descriptive statistics of the data. The distribution of variables was measured by Kolmogorov–Smirnov test. Mann–Whitney U-test and Kruskal–Wallis H-test were used for the analysis of quantitative independent data, and Chi-square test was used for the analysis of qualitative independent data. Fisher's exact test was used when the Chi-square test conditions were not met. Univariate and multivariate binary logistic regression analysis was used to determine the risk factors. For all analyses, the level of significance was accepted as P < 0.05 in the 95% confidence interval.
| Results|| |
While the length of stay in ICU of 361 patients was <1 day, 39 patients' length of stay was more than 1 day. When SICN and LICN groups were compared in terms of preoperative data such as demographic data, smoking history (P > 0.999), alcohol use (P = 0.617), antidepressant and benzodiazepine use history (P > 0.999), glioma tumor presence (P = 0.410), and concomitant disease (hypertension, CAD, arrhythmia, ischemic stroke, and history of diabetes) (P = 0.577) did not show a significant difference (P > 0.05). Age was significantly lower in the SICN group compared to the LCIN group (P = 0.049) [Table 1].
|Table 1: The relationship between the preoperative parameters of the patients and the length of stay in intensive care unit|
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The distribution of perioperative data such as duration of anesthesia, fluid balance, amount of hemorrhage, and blood transfusion was found to be significantly lower (P < 0.001) in the SICN group than in the LICN group. The duration of anesthesia was 5.76 ± 1.72 h in the SICN group and 8.12 ± 3.03 h in the LCIN group. The fluid balance was 1530.27 ± 637.78 ml in the SCIN group and 2012.82 ± 845.01 in the LICN group; the amount of bleeding was 490.61 ± 641.73 ml in the SICN group and 980.77 ± 1108.67 ml in the LICN group. When the groups were evaluated in terms of blood transfusion, it was found that blood transfusion was done in 25.76% of patients in the SICN group and 61.54% of patients in the LICN group.
Significant differences were found between the groups in terms of TIVA use during the operation (P < 0.001), tumor localization (P = 0.013), and frontal approach (P = 0.025).
Groups were compared in terms of the data obtained during the admission of the patients to intensive care. During the admission to ICU, 19.39% of patients were intubated in the SICN group and 82.05% in the LICN group. In the LICN group, 23.08% of the patients required MV, while its requirement was only 1.39% in the SICN group (P < 0.001). The GCS score calculated in admission to ICU was also significantly lower (P < 0.001) in the LCIN group than in the SICN group. While 74.36% of patients in the LICN group had low GCS scores, 64.27% of patients in the SICN group had high GCS scores. On comparison of patients with SpO2<90%, serum glucose level ≥10 mmol/l (P = 0.013), and high serum glucose levels without diabetes (P = 0.005), there was a statistically significant difference between the groups [Table 2].
|Table 2: The relationship between the perioperative parameters of the patients and the length of stay in intensive care unit|
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When the SICN and LICN groups were compared in terms of intensive care follow-up data, endotracheal tube duration was significantly lower in the SICN group than in the LICN group (P < 0.001). However, there was no notable difference (P = 0.203) between the groups in terms of duration of MV. There was a statistically significant difference between the groups in patients who developed hematoma (P = 0.008), a serious complication encountered in the intensive care follow-up and the incidence of reintubation (P = 0.026) [Table 3].
|Table 3: The relationship between the patients' intensive care follow-up parameters and the length of stay in intensive care unit|
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Univariate binary regression analysis was performed for each of the independent variables, and the coefficients of the independent variables that were significant in the analysis were given in [Table 4].
|Table 4: Coefficient estimation of variables for univariate binary logistic regression model|
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A test was carried out to see if there is a correlation between the variables that were found to have a significant effect on the result of univariate binary regression analysis. There was a correlation between the duration of ICU stay and frontal approach and tumor localization, the presence of endotracheal tube and GCS score, serum glucose ≥10 mmol/l, and high serum glucose without diabetes (correlation coefficient >0.6). The multivariate regression analysis was applied to the variables that were found to be significant as a result of univariate regression analysis with forward selection method (only one of the variables that were correlated was taken). The information regarding the coefficients of the model designed as a result of this method is given in [Table 5].
|Table 5: Coefficient estimation of covariates for multivariate binary logistic regression model|
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In the omnibus test results regarding the created logistic regression model, model Chi-square statistics were found to be significant (χ2 = 106.264, df = 7, P < 0.001). The degree of correlation between the dependent variable and the independent variable was 23.3% according to Cox-Snell R2 and 49.4% according to Nagelkerke R2. Using the Hosmer–Lemeshow test, it was found that the fit of the model was sufficient (χ2 = 14.112, df = 8, P < 0.079). In operations performed with TIVA, the probability of SICN is almost 30 times higher as compared to operations performed without TIVA.
| Discussion|| |
As a result of our study, after brain tumor resection, duration of anesthesia, use of TIVA, presence of endotracheal tube on admission to ICU, MV requirement, postoperative pain control with PCA device, and postoperative hematoma were found to be important predictors for the length of stay in ICU. In addition, age, frontal approach in surgery, tumor localization, fluid balance, intraoperative bleeding and blood transfusion, SpO2 <90% in admission to ICU, being serum glucose ≥10 mmol/l, high serum glucose without diabetes, GCS score, endotracheal tube duration, and reintubation status were found to be useful in determining LICN.
It is recommended that all patients who undergo craniotomy for brain tumor should receive intensive care for 12–24 h to be followed closely. In our study too, 361 of 400 patients were transferred to the service after being followed up in ICU for 1 day. Only 39 (9.75%) patients required LICN. There are a limited number of studies in which patients are evaluated in terms of intensive care needs after craniotomy.,,, In a study conducted by Sarkissian and Wallace, a higher rate of patients (31%) than in our study remained in ICU for more than 1 day while results similar to our study were found by Ziai et al., with 15% of the patients who needed long intensive care duration. Sarkissian and Wallace showed that tumor type, intubation on admission to ICU, and postoperative complications were as LICN-related factors. Ziai et al. revealed that the need for longer ICU stay could be estimated with radiological findings, large amounts of intraoperative blood loss, fluid requirement, and decision of patients' admission to ICU as intubated at the end of the operation. In a study in pediatric patients, blood loss and intubation in ICU admission were shown to be the parameters that can be used to predict LICN. Hanak et al. also found that patients who were diabetic, who had more intraoperative blood loss and had needed blood transfusion, who were older, and who needed prolonged anesthesia were more likely to need intensive care. Similar to these studies, age, duration of anesthesia, intraoperative bleeding, blood transfusion, fluid balance, being intubated at ICU admission, MV need, and serious postoperative complication were found to be associated with LICN in our study too. On the contrary, LICN was determined to be 2-fold in patients using no PCA and 30-fold in patients who underwent balanced anesthesia instead of TIVA in our study.
When we evaluated the significance of independent variables in the model designed with regression analysis that indicates the factors affecting the length of stay in ICU, we found that they were all significant (P < 0.05). In the designed model, the coefficients of TIVA and PCA are positive, and the increase in these applications decreases the patients' LICN probability. The coefficients of variables such as MV requirement, hematoma, endotracheal tube, and duration of anesthesia variables are all negative (−2.280, −2.657, −2.405, and −0.0207, respectively). Therefore, there is an increased likelihood of LICN for patients who are intubated on ICU admission, the need for MV, postoperative hematoma, and prolonged anesthesia duration.
One of the effective factors in the predicting model created is the duration of anesthesia. Prolonged surgery and anesthesia duration are among the risk factors associated with delay in recovery. Longer duration of anesthesia, due to a larger operation, may indicate more negative outcomes. Routh et al. found that longer duration of anesthesia (especially longer than 6 h) was associated with increased incidence of postoperative complications and mortality.
In our model, the likelihood of LICN in patients undergoing TIVA is lower and it is similar to the results of a study that was carried out on patients who underwent cardiac surgery. Likhvantsev et al. found that volatile anesthetics decreased the hospitalization duration. In a review by Chui et al., anesthesia with volatile agents and TIVA were compared during elective craniotomy, and intracranial pressure was found to be lower and cerebral perfusion pressure was determined to be higher in the TIVA group.
Postoperative pain after craniotomy is an important parameter and needs to be checked. Studies have shown that good pain management shortens the length of stay in ICU. Our study also showed that patients provided with pain control through PCA had lower LICN. In our study, the combination of analgesic (sufentanil) and antiemetic (tropisetron) was used in patients with PCA device. Because the opioid analgesics such as sufentanil can cause confusion, the PCA was performed in patients with postoperative consciousness.
Being intubated on ICU admission and MV requirement were also found to be associated with LICN in our study. A similar result was obtained by two other studies, that determined in patients with coronary artery bypass grafting that the longer intubation time and MV duration of more than 24 h increased the length of stay in ICU. In another study, Cislaghi et al. demonstrated that prolonged MV was associated with prolonged intensive care and total hospital stay.
Intracerebral hematoma, which is the most feared and the most important complication after neurosurgery operations, was one of the factors determining LICN in our model. 12.82% of the patients in the LICN group and 3.6% of the SCIN group developed hematoma in our study. Numerous studies have shown a significant relationship between intracerebral hematoma and mortality/morbidity. It was found in our study that the likelihood of LICN increased in the case of postoperative hematoma as well.
In our study, it is seen that the parameters determined to be related to LICN as a result of univariate regression analysis are similar to those of previous studies, except for the model obtained from multivariate binary regression analysis. Kinnunen et al. investigated the effect of perioperative bleeding and blood transfusion on prognosis in patients with coronary artery bypass grafting. Minor hemorrhage and 1–2 units of erythrocyte transfusion were shown to be associated with prolonged ICU stay. In a study by Salma et al., it was demonstrated that blood loss and blood transfusion after orthognathic surgery increased the hospital stay. Our study also indicated that intraoperative blood loss and increased blood transfusion are associated with LICN. It is thought that the advanced age factor will have a negative effect on the surgical results due to the increase in the accompanying diseases. Similarly, our study signifies that the mean age of the patients in the LICN group was significantly higher than in the SICN group. In a review of the factors affecting the length of stay in ICU, a large number of studies in patients admitted in intensive care after cardiac surgery were discussed. As a result of this review, variables such as advanced age and renal failure were found to affect the length of stay in ICU in most studies. Liu et al. recently conducted a study on serum glucose elevation being one of the factors found to be associated with LICN by our study. Liu et al. found that in the intracranial tumor surgery, intraoperative glucose level fluctuations were related to the increased levels of interleukin-6, tumor necrosis factor-alpha, and C-reactive protein, and smaller fluctuations were associated with better prognosis. One of the parameters in our study that can be used to estimate LICN was determined to be GCS score in ICU admission. GCS, which has been developed in order to determine the severity of trauma and status of consciousness in patients with head trauma, is also used for prognosis in other critically ill patients. In particular, this is helpful in rapid decision-making situations such as the patients' airway management or accepting the patient into intensive care.
A limitation of our study is that this study is performed only in patients admitted to ICU after brain tumor surgery; therefore, the detected parameters should not be applied to other patients. Furthermore, the relatively small number of LICN patients may affect the statistical interpretation of some variables.
Further studies with more patients are needed to apply these parameters to other intensive care populations. In addition, new studies can be conducted to examine the relationship between other scoring systems and parameters and length of stay in ICU.
| Conclusions|| |
In this study, preoperative, perioperative, and intensive care follow-up parameters which can be used to estimate the length of stay in ICU in patients after brain tumor surgery are determined. Advanced age, one of the preoperative parameters, is important in determining the length of stay in ICU. Parameters such as duration of anesthesia, fluid balance, bleeding and blood transfusion, TIVA application, and use of PCA device are also important perioperative parameters that can be used to predict LICN. In addition, intubation status in the admission to ICU, MV requirement, serum glucose level ≥10 mmol/l, low GCS score, and development of hematoma during intensive care follow-up are among the risk factors affecting the length of stay in ICU. These findings will help to make more accurate use of intensive care resources and will help to make surgical planning easily of high-risk patients who need postoperative intensive care.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]